R语言 saemix包 saemix.plots()函数中文帮助文档(中英文对照)

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saemix.plots(saemix)
saemix.plots()所属R语言包：saemix

                                         General plot function from SAEM
                                         一般的绘图功能SAEM

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

Several plots (selectable by the type argument) are currently available: convergence plot, individual plots, predictions versus observations, distribution plots, VPC, residual plots.
几个图（可选择的类型参数）是目前可供选择：收敛的图，个别图，预测与观察，分布图，VPC，残差图。


用法----------Usage----------


plot(x,y, ...)



参数----------Arguments----------

参数：x
an object returned by the saemix function
返回的对象saemix的函数


参数：y
empty
空的


参数：...
optional arguments passed to the plots
可选参数传递给该图


Details

详细信息----------Details----------

This is the generic plot function for an SaemixObject object, which implements different graphs related to the algorithm (convergence plots, likelihood estimation) as well as diagnostic graphs. A description is provided in the PDF documentation. Arguments such as main, xlab, etc... that can be given to the generic plot function may be used, and will be interpreted according to the type of plot that is to be drawn.
这是一个通用的绘图功能的SaemixObject对象，它实现了不同的图形相关的算法（收敛的图，似然估计），以及诊断图表。说明中提供的PDF文档。参数，如主，xlab，等等。可以给通用plot函数也可以使用，并且，根据要被绘制的图类型将被解释。

A special argument plot.type can be set to determine the type of plot; it can be one of:   
一个特殊的参数plot.type可以设置以确定图的类型，它可以是：

data:A spaghetti plot of the data, displaying the observed data y as a function of the regression variable (time for a PK application)
数据：A意大利面条的数据曲线，作为回归变量的函数（一个PK应用程序的时间显示所观察到的数据y）

convergence:For each parameter in the model, this plot shows the evolution of the parameter estimate versus the iteration number
收敛：对于每一个模型中的参数，此图显示的参数估计与迭代次数的演变

likelihood:Graph showing the evolution of the log-likelihood during the estimation by importance sampling
可能性：图表显示在进化过程中的对数似然估计的重要性采样

observations.vs.predictionslot of the predictions computed with the population parameters versus the observations (left), and plot of the predictions computed with the individual parameters versus the observations (right)
observations.vs.predictions：图人口参数与观察（左），和图的预测与观察各个参数与计算计算的预测（右）

residuals.scatter:Scatterplot of the residuals versus the predictor (top) and versus predictions (bottom), for weighted residuals (population residuals, left), individual weighted residuals (middle) and npde (right).
residuals.scatter：残差与预测（上）和与预测（下），加权残值法（人口残差左），个别加权残值法（中）和npde（右）的散点图。

residuals.distributionistribution of the residuals, plotted as histogram (top) and as a QQ-plot (bottom), for weighted residuals (population residuals, left), individual weighted residuals (middle) and npde (right).
residuals.distribution：残差分布，绘制直方图（上）和一个QQ图（下），加权残值法（人口残差左），个别加权残值法（中）和npde（右）。

individual.fit:Individual fits are obtained using the individual parameters with the individual covariates
individual.fit：个人适合使用单独的参数与个人的协变量

population.fitopulation fits are obtained using the population parameters with the individual covariates
population.fit：人口千篇一律的使用人口参数与个人的协变量

both.fit:Individual fits, superposing fits obtained using the population parameters with the individual covariates (red) and using the individual parameters with the individual covariates (green)
both.fit：个人配合，叠加要让使用人口参数与个人的协变量（红色），并使用单独的参数与个人的协变量（绿色）

marginal.distributionistribution of the parameters (conditional on covariates when some are included in the model). A histogram of individual parameter estimates can be overlayed on the plot, but it should be noted that the histogram does not make sense when there are covariates influencing the parameters and a warning will be displayed
marginal.distribution：分布参数（有条件时，一些包含在模型中的协变量）。个别参数估计的柱状图可以被覆盖的图，但应注意，直方图没有任何意义时，有协变量的影响参数，将显示警告

random.effects:Boxplot of the random effects
random.effects：盒形图的随机效应

correlations:Correlation between the random effects
相关性：随机效应之间的相关性

parameters.vs.covariateslots of the estimates of the individual parameters versus the covariates, using scatterplot for continuous covariates, boxplot for categorical covariates
的parameters.vs.covariates图的各个参数的估计与协变量，使用连续的变量散点图，盒形图分类协变量

randeff.vs.covariateslots of the estimates of the random effects versus the covariates, using scatterplot for continuous covariates, boxplot for categorical covariates
的randeff.vs.covariates图与协变量的随机效应的估计，使用连续的变量散点图，盒形图分类协变量

npdelots 4 graphs to evaluate the shape of the distribution of the normalised prediction distribution errors (npde)
npde：图解4的归一化预测分布误差的分布的形状的图形以评估（npde）

vpc:Visual Predictive Check, with options to include the prediction intervals around the boundaries of the selected interval as well as around the median (50th percentile of the simulated data).    In addition, the following values for plot.type produce a series of plots:   
的VPC：视觉预测查看，选项包括预测间隔的边界周围的选择的时间间隔，以及周围的位数（模拟数据的第50百分位数）。此外，下面的为plot.type值产生一系列的图：

reduced: produces the following plots: plot of the data, convergence plots, plot of the likelihood by importance sampling (if computed), plots of observations versus predictions. This is the default behaviour of the plot function applied to an SaemixObject object
降低：以下图：图的数据，收敛图，绘图的重要性采样的可能性（如果计算），图的观察与预测。这是图功能应用到一个SaemixObject对象的默认行为

full: produces the following plots: plot of the data, convergence plots, plot of the likelihood by importance sampling (if computed), plots of observations versus predictions, scatterplots and distribution of residuals, VPC, npde, boxplot of the random effects, distribution of the parameters, correlations between random effects, plots of the relationships between individually estimated parameters and covariates, plots of the relationships between individually estimated random effects and covariates
全：以下图：图的数据，收敛图，绘图的重要性采样的可能性（如果计算），图的观察与预测，散点图和残差分布，VPC，npde，盒形图的随机效应，分布的参数，随机效应之间的相关性，图的个别估计参数和协变量之间的关系，单独估计的随机效应和协变量之间的关系图

  Each plot can be customised by modifying options, either through a list of options set by the saemix.plot.setoptions function, or on the fly by passing an option in the call to the plot (see examples).
每个小区可以自定义修改选项，通过设置的选项列表saemix.plot.setoptions功能，或在飞行中通过调用的图中的一个选项（参见示例）。


值----------Value----------

None
无


（作者）----------Author(s)----------


Emmanuelle Comets <emmanuelle.comets@inserm.fr>, Audrey Lavenu, Marc Lavielle.




参考文献----------References----------


Monolix32_UsersGuide.pdf (http://software.monolix.org/sdoms/software/)

参见----------See Also----------

SaemixObject,saemix, saemix.plot.setoptions, saemix.plot.select, saemix.plot.data
SaemixObject，saemix，saemix.plot.setoptions，saemix.plot.select，saemix.plot.data


实例----------Examples----------


data(theo.saemix)

saemix.data<-saemixData(name.data=theo.saemix,header=TRUE,sep=" ",na=NA,
  name.group=c("Id"),name.predictors=c("Dose","Time"),
  name.response=c("Concentration"),name.covariates=c("Weight","Sex"),
  units=list(x="hr",y="mg/L",covariates=c("kg","-")), name.X="Time")

model1cpt<-function(psi,id,xidep) {
          dose<-xidep[,1]
          tim<-xidep[,2]  
          ka<-psi[id,1]
          V<-psi[id,2]
          CL<-psi[id,3]
          k<-CL/V
          ypred<-dose*ka/(V*(ka-k))*(exp(-k*tim)-exp(-ka*tim))
          return(ypred)
}

saemix.model<-saemixModel(model=model1cpt,
  description="One-compartment model with first-order absorption",
  psi0=matrix(c(1.,20,0.5,0.1,0,-0.01),ncol=3, byrow=TRUE,
  dimnames=list(NULL, c("ka","V","CL"))),transform.par=c(1,1,1),
  covariate.model=matrix(c(0,1,0,0,0,0),ncol=3,byrow=TRUE),fixed.estim=c(1,1,1),
  covariance.model=matrix(c(1,0,0,0,1,0,0,0,1),ncol=3,byrow=TRUE),
  omega.init=matrix(c(1,0,0,0,1,0,0,0,1),ncol=3,byrow=TRUE),error.model="constant")

saemix.options<-list(seed=632545,save=FALSE,save.graphs=FALSE)

saemix.fit<-saemix(saemix.model,saemix.data,saemix.options)

# Set of default plots[设置的默认图]
plot(saemix.fit)

# Data[数据]
plot(saemix.fit,plot.type="data")

# Convergence[收敛]
plot(saemix.fit,plot.type="convergence")

# Individual plot for subject 1, smoothed[个别图为主题，平滑]
plot(saemix.fit,plot.type="individual.fit",ilist=1,smooth=TRUE)

# Individual plot for subject 1 to 12, with ask set to TRUE [主题1到12个人的图，与要求设置为TRUE]
# (the system will pause before a new graph is produced)[（系统会暂停之前产生一个新的图形）]
plot(saemix.fit,plot.type="individual.fit",ilist=c(1:12),ask=TRUE)

# Diagnostic plot: observations versus population predictions[诊断的图：观察与人口预测]
par(mfrow=c(1,1))
plot(saemix.fit,plot.type="observations.vs.predictions",level=0,new=FALSE)

# LL by Importance Sampling[LL重要性抽样]
plot(saemix.fit,plot.type="likelihood")

# Scatter plot of residuals[对残差的散点图]
# Data will be simulated to compute weighted residuals and npde[将模拟数据计算加权残值法和npde]
# the results shall be silently added to the object saemix.fit[结果被悄悄地加入的对象saemix.fit的]
plot(saemix.fit,plot.type="residuals.scatter")

# Boxplot of random effects[盒形图的随机效应]
plot(saemix.fit,plot.type="random.effects")

# Relationships between parameters and covariates[参数和协变量之间的关系]
plot(saemix.fit,plot.type="parameters.vs.covariates")

# Relationships between parameters and covariates, on the same page[参数和协变量之间的关系，在同一页上]
par(mfrow=c(3,2))
plot(saemix.fit,plot.type="parameters.vs.covariates",new=FALSE)

# VPC[VPC]
plot(saemix.fit,plot.type="vpc")

转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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